Rajesh Vakkalagadda needs a sample:
Sampling is a fundamental process in machine learning that involves selecting a subset of data from a larger dataset. This technique is used to make training and evaluation more efficient, especially when working with massive datasets where processing every data point is impractical
However, sampling comes with its own challenges. Ensuring that samples are representative is crucial to prevent biases that could lead to poor model generalization and inaccurate evaluation results. The sample size must strike a balance between performance and resource constraints. Additionally, sampling strategies need to account for factors such as class imbalance, temporal dependencies, and other dataset-specific characteristics to maintain data integrity.
Click through for an answer in Scala. The Python implementation would be very similar,